1. Technical Field
The present disclosure relates to segmentation in MRI and, more specifically, to robust segmentation of breast and muscle in MRI.
2. Discussion of Related Art
Computer aided diagnosis (CAD) is the process of using computer vision systems to analyze medical image data and make a determination as to what regions of the image data are potentially problematic. Some CAD techniques then present these regions of suspicion to a medical professional such as a radiologist for manual review, while other CAD techniques make a preliminary determination as to the nature of the region of suspicion. For example, some CAD techniques may characterize each region of suspicion as a lesion or a non-lesion. The final results of the CAD system may then be used by the medical professional to aid in rendering a final diagnosis.
Because CAD techniques may identify lesions that may have been overlooked by a medical professional working without the aid of a CAD system, and because CAD systems can quickly direct the focus of a medical professional to the regions most likely to be of diagnostic interest, CAD systems may be highly effective in increasing the accuracy of a diagnosis and decreasing the time needed to render diagnosis. Accordingly, scarce medical resources may be used to benefit a greater number of patients with high efficiency and accuracy.
CAD techniques have been applied to the field of mammography, where low-dose x-rays are used to image a patient's breast to diagnose suspicious breast lesions. However, because mammography relies on x-ray imaging, mammography may expose a patient to potentially harmful ionizing radiation. As many patients are instructed to undergo mammography on a regular basis, the administered ionizing radiation may, over time, pose a risk to the patient. Moreover, it may be difficult to use x-rays to differentiate between different forms of masses that may be present in the patient's breast. For example, it may be difficult to distinguish between calcifications and malignant lesions.
Magnetic resonance imaging (MRI) is a medical imaging technique that uses a powerful magnetic field to image the internal structure and certain functionality of the human body. MRI is particularly suited for imaging soft tissue structures and is thus highly useful in the field of oncology for the detection of lesions.
In dynamic contrast enhanced MRI (DCE-MRI), many additional details pertaining to bodily soft tissue may be observed. These details may be used to further aid in diagnosis and treatment of detected lesions.
DCE-MRI may be performed by acquiring a sequence of MR images that span a time before magnetic contrast agents are introduced into the patient's body and a time after the magnetic contrast agents are introduced. For example, a first MR image may be acquired prior to the introduction of the magnetic contrast agents, and subsequent MR images may be taken at a rate of one image per minute for a desired length of time. By imaging the body in this way, a set of images may be acquired that illustrate how the magnetic contrast agent is absorbed and washed out from various portions of the patient's body. This absorption and washout information may be used to characterize various internal structures within the body and may provide additional diagnostic information.
However, when imaging the breast for the purposes of performing computer-assisted detection of potential breast lesions, for example, using DCE-MRI, tissue such as muscle may appear with a high level of enhancement that may complicate the search for potential breast lesions. Accordingly, it may be beneficial to first segment the acquired medical image data to isolate the breast tissue from the surrounding musculature.
Because of its close proximity to the breast, the pectoral muscle may segmented and removed from the acquired medical image data prior to analyzing the breast tissue for potential lesions. It may be also beneficial to detect the invasion of the muscle by a cancer by judging whether the cancer touches the muscle.
Segmentation of the pectoral muscle, however, may be particularly difficult owing to the relatively unclear boundaries that are obtained by performing MR imaging on the pectoral muscle.